CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents

• URL: http://arxiv.org/abs/2505.13044v1
• Date: Mon, 19 May 2025 12:33:52 GMT
• Title: CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents
• Authors: Rebecca Westhäußer, Frederik Berenz, Wolfgang Minker, Sebastian Zepf,
• Abstract summary: Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems.<n>They still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment.<n>To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across interaction sessions.<n> Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision-making.
• Score: 1.6082737760346446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across interaction sessions for suitable responses. Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision-making, that can contribute towards improved memory modeling for LLMs. Inspired by these cognitive AI principles, we propose our memory framework CAIM. CAIM consists of three modules: 1.) The Memory Controller as the central decision unit; 2.) the Memory Retrieval, which filters relevant data for interaction upon request; and 3.) the Post-Thinking, which maintains the memory storage. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, contextual coherence, and memory storage. The results demonstrate that CAIM outperforms baseline frameworks across different metrics, highlighting its context-awareness and potential to improve long-term human-AI interactions.

Related papers

• MemOS: A Memory OS for AI System [116.87568350346537]
  Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI)<n>Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.<n>MemOS is a memory operating system that treats memory as a manageable system resource.
  arXiv  Detail & Related papers  (2025-07-04T17:21:46Z)
• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents [84.62985963113245]
  We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks.<n>At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning.<n>We show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task.
  arXiv  Detail & Related papers  (2025-06-18T19:44:46Z)
• FindingDory: A Benchmark to Evaluate Memory in Embodied Agents [49.89792845476579]
  We introduce a new benchmark for long-range embodied tasks in the Habitat simulator.<n>This benchmark evaluates memory-based capabilities across 60 tasks requiring sustained engagement and contextual awareness.
  arXiv  Detail & Related papers  (2025-06-18T17:06:28Z)
• Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems [0.0]
  This paper introduces Contextual Memory Intelligence (CMI) as a new paradigm for building intelligent systems.<n> CMI repositions memory as an adaptive infrastructure necessary for longitudinal coherence, explainability, and responsible decision-making.<n>This enhances human-AI collaboration, generative AI design, and the resilience of the institutions.
  arXiv  Detail & Related papers  (2025-05-28T18:59:16Z)
• From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs [34.361000444808454]
  Memory is the process of encoding, storing, and retrieving information.<n>In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions.
  arXiv  Detail & Related papers  (2025-04-22T15:05:04Z)
• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [133.45145180645537]
  The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence.<n>As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges.<n>This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• AI-native Memory 2.0: Second Me [26.425003835524123]
  SECOND ME acts as an intelligent, persistent memory offload system.<n>It can generate context-aware responses, prefill required information, and facilitate seamless communication with external systems.<n>Second ME further represents a critical step toward augmenting human-world interaction with persistent, contextually aware, and self-optimizing memory systems.
  arXiv  Detail & Related papers  (2025-03-11T07:05:52Z)
• In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents [70.12342024019044]
  Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information limits their effectiveness.<n>We propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections.<n>RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
  arXiv  Detail & Related papers  (2025-03-11T04:15:52Z)
• InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions [104.90258030688256]
  This project introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input.<n>This project simulates human-like cognition, enabling multimodal large language models to provide continuous and adaptive service over time.
  arXiv  Detail & Related papers  (2024-12-12T18:58:30Z)
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
  We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent)<n>It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.<n>The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
  arXiv  Detail & Related papers  (2024-06-09T21:58:32Z)
• Empowering Working Memory for Large Language Model Agents [9.83467478231344]
  This paper explores the potential of applying cognitive psychology's working memory frameworks to large language models (LLMs) An innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios.
  arXiv  Detail & Related papers  (2023-12-22T05:59:00Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• A Framework for Inference Inspired by Human Memory Mechanisms [9.408704431898279]
  We propose a PMI framework that consists of perception, memory and inference components. The memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. We apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets.
  arXiv  Detail & Related papers  (2023-10-01T08:12:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.